Passive membrane transport of lignin-related compounds
Bibliographic record
Abstract
Lignin is an abundant aromatic polymer found in plant secondary cell walls. In recent years, lignin has attracted renewed interest as a feedstock for bio-based chemicals via catalytic and biological approaches and has emerged as a target for genetic engineering to improve lignocellulose digestibility by altering its composition. In lignin biosynthesis and microbial conversion, small phenolic lignin precursors or degradation products cross membrane bilayers through an unidentified translocation mechanism prior to incorporation into lignin polymers (synthesis) or catabolism (bioconversion), with both passive and transporter-assisted mechanisms postulated. To test the passive permeation potential of these phenolics, we performed molecular dynamics simulations for 69 monomeric and dimeric lignin-related phenolics with 3 model membranes to determine the membrane partitioning and permeability coefficients for each compound. The results support an accessible passive permeation mechanism for most compounds, including monolignols, dimeric phenolics, and the flavonoid, tricin. Computed lignin partition coefficients are consistent with concentration enrichment near lipid carbonyl groups, and permeability coefficients are sufficient to keep pace with cellular metabolism. Interactions between methoxy and hydroxy groups are found to reduce membrane partitioning and improve permeability. Only carboxylate-modified or glycosylated lignin phenolics are predicted to require transporters for membrane translocation. Overall, the results suggest that most lignin-related compounds can passively traverse plant and microbial membranes on timescales commensurate with required biological activities, with any potential transport regulation mechanism in lignin synthesis, catabolism, or bioconversion requiring compound functionalization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".